Recommendations for the integration of genomics into clinical practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The introduction of diagnostic clinical genome and exome sequencing (CGES) is changing the scope of practice for clinical geneticists. Many large institutions are making a significant investment in infrastructure and technology, allowing clinicians to access CGES, especially as health-care coverage begins to extend to clinically indicated genomic sequencing-based tests. Translating and realizing the comprehensive clinical benefits of genomic medicine remain a key challenge for the current and future care of patients. With the increasing application of CGES, it is necessary for geneticists and other health-care providers to understand its benefits and limitations in order to interpret the clinical relevance of genomic variants identified in the context of health and disease. New, collaborative working relationships with specialists across diverse disciplines (e.g., clinicians, laboratorians, bioinformaticians) will undoubtedly be key attributes of the future practice of clinical genetics and may serve as an example for other specialties in medicine. These new skills and relationships will also inform the development of the future model of clinical genetics training curricula. To address the evolving role of the clinical geneticist in the rapidly changing climate of genomic medicine, two Clinical Genetics Think Tank meetings were held that brought together physicians, laboratorians, scientists, genetic counselors, trainees, and patients with experience in clinical genetics, genetic diagnostics, and genetics education. This article provides recommendations that will guide the integration of genomics into clinical practice.Genet Med 18 11, 1075-1084.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it